What is a GIS?
A GIS is a computer system capable of capturing, storing, analyzing, and displaying geographically referenced information; that is, data identified according to location. Practitioners also define a GIS as including the procedures, operating personnel, and spatial data that go into the system.
How does a GIS work?
Relating information from different sources
The power of a GIS comes from the ability to relate different information in a spatial context and to reach a conclusion about this relationship. Most of the information we have about our world contains a location reference, placing that information at some point on the globe. When rainfall information is collected, it is important to know where the rainfall is located. This is done by using a location reference system, such as longitude and latitude, and perhaps elevation. Comparing the rainfall information with other information, such as the location of marshes across the landscape, may show that certain marshes receive little rainfall. This fact may indicate that these marshes are likely to dry up, and this inference can help us make the most appropriate decisions about how humans should interact with the marsh. A GIS, therefore, can reveal important new information that leads to better decisionmaking.
Many computer databases that can be directly entered into a GIS are being produced by Federal, State, tribal, and local governments, private companies, academia, and nonprofit organizations. Different kinds of data in map form can be entered into a GIS (figs. 1a, 1b, 1c, 1d, 1e, 1f, and 2). A GIS can also convert existing digital information, which may not yet be in map form, into forms it can recognize and use. For example, digital satellite images can be analyzed to produce a map of digital information about land use and land cover (figs. 3 and 4). Likewise, census or hydrologic tabular data can be converted to a maplike form and serve as layers of thematic information in a GIS (figs. 5 and 6).
Vector vs. raster data:
A basic classification scheme of geometrical modelling techniques is based on the internal representation of the data. Topographic objects can be represented by:
1.
Vector data: The representation of the objects is based on distinct points described by their co-ordinates in the reference system and their topological relations, especially edges (connections of two points) and surfaces (e.g. represented by closed loops of edges, see below). Vector representations are very compact and thus do not require much disk space. In addition, operations such as geometrical transformations or visualization can be performed rather fast. However, in some situations, e.g. in the context of the union of thematic layers, more complex algorithms have to be applied than with raster data defined on a common grid.
2.
Raster data: The representation of the objects is based on the elements of a (2D or 3D) matrix. The geometry of such an element (a grid point or pixel) is given by the row and column indices of that element, the offset of the first (e.g. the upper left) pixel of the matrix, and the grid interval. There are two views on the meaning of a pixel in a raster model. First, the pixel can be seen to represent a singular grid point. In this case, the rectangular area enclosed by four neighbouring grid points is called a facet of the raster model. From another point of view, the pixel can be seen to represent a rectangular area itself in an integral manner. The value assigned to a pixel describes one thematic attribute of that pixel. Topological information is only contained implicitly: neighbourhood relations can be described by index differences. Topographic objects can only be represented by a set of neighbouring pixels having identical attributes. Thus, the manipulation of individual objects is very difficult. However, the structure of raster data is simple, and operations requiring information on surface coverage can be performed rather easily. This is also true for data acquisition which can, for instance, be performed by classification of satellite images. These benefits are contrasted by the enormous requirements for data storage (especially for continuous tone data and in particular for 3D raster data) and the high computational costs for tasks such as geometrical transformations.
A GIS is a computer system capable of capturing, storing, analyzing, and displaying geographically referenced information; that is, data identified according to location. Practitioners also define a GIS as including the procedures, operating personnel, and spatial data that go into the system.
How does a GIS work?
Relating information from different sources
The power of a GIS comes from the ability to relate different information in a spatial context and to reach a conclusion about this relationship. Most of the information we have about our world contains a location reference, placing that information at some point on the globe. When rainfall information is collected, it is important to know where the rainfall is located. This is done by using a location reference system, such as longitude and latitude, and perhaps elevation. Comparing the rainfall information with other information, such as the location of marshes across the landscape, may show that certain marshes receive little rainfall. This fact may indicate that these marshes are likely to dry up, and this inference can help us make the most appropriate decisions about how humans should interact with the marsh. A GIS, therefore, can reveal important new information that leads to better decisionmaking.
Many computer databases that can be directly entered into a GIS are being produced by Federal, State, tribal, and local governments, private companies, academia, and nonprofit organizations. Different kinds of data in map form can be entered into a GIS (figs. 1a, 1b, 1c, 1d, 1e, 1f, and 2). A GIS can also convert existing digital information, which may not yet be in map form, into forms it can recognize and use. For example, digital satellite images can be analyzed to produce a map of digital information about land use and land cover (figs. 3 and 4). Likewise, census or hydrologic tabular data can be converted to a maplike form and serve as layers of thematic information in a GIS (figs. 5 and 6).
Vector vs. raster data:
A basic classification scheme of geometrical modelling techniques is based on the internal representation of the data. Topographic objects can be represented by:
1.
Vector data: The representation of the objects is based on distinct points described by their co-ordinates in the reference system and their topological relations, especially edges (connections of two points) and surfaces (e.g. represented by closed loops of edges, see below). Vector representations are very compact and thus do not require much disk space. In addition, operations such as geometrical transformations or visualization can be performed rather fast. However, in some situations, e.g. in the context of the union of thematic layers, more complex algorithms have to be applied than with raster data defined on a common grid.
2.
Raster data: The representation of the objects is based on the elements of a (2D or 3D) matrix. The geometry of such an element (a grid point or pixel) is given by the row and column indices of that element, the offset of the first (e.g. the upper left) pixel of the matrix, and the grid interval. There are two views on the meaning of a pixel in a raster model. First, the pixel can be seen to represent a singular grid point. In this case, the rectangular area enclosed by four neighbouring grid points is called a facet of the raster model. From another point of view, the pixel can be seen to represent a rectangular area itself in an integral manner. The value assigned to a pixel describes one thematic attribute of that pixel. Topological information is only contained implicitly: neighbourhood relations can be described by index differences. Topographic objects can only be represented by a set of neighbouring pixels having identical attributes. Thus, the manipulation of individual objects is very difficult. However, the structure of raster data is simple, and operations requiring information on surface coverage can be performed rather easily. This is also true for data acquisition which can, for instance, be performed by classification of satellite images. These benefits are contrasted by the enormous requirements for data storage (especially for continuous tone data and in particular for 3D raster data) and the high computational costs for tasks such as geometrical transformations.
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